Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage
| Autor: | W. Peter Vandertop, Silvia D. Olabarriaga, Charles B. L. M. Majoie, Wessel E. van der Steen, I Jsbrand Andreas Jan Zijlstra, Henk A. Marquering, Renan Sales Barros, Dagmar Verbaan, A. H. Zwinderman, Gustav J. Strijkers, René van den Berg, Lucas Alexandre Ramos |
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| Přispěvatelé: | Neurosurgery, ACS - Atherosclerosis & ischemic syndromes, ACS - Microcirculation, ANS - Neurovascular Disorders, Graduate School, AMS - Restoration & Development, Biomedical Engineering and Physics, Radiology and Nuclear Medicine, CCA - Cancer Treatment and Quality of Life, Epidemiology and Data Science, APH - Methodology, AMS - Sports & Work, ACS - Heart failure & arrhythmias |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Subarachnoid hemorrhage
Ischemia Logistic regression Machine learning computer.software_genre Sensitivity and Specificity 030218 nuclear medicine & medical imaging Brain Ischemia Cohort Studies Machine Learning 03 medical and health sciences 0302 clinical medicine Aneurysm Predictive Value of Tests medicine Humans In patient Prospective Studies Lead (electronics) Severe complication business.industry Area under the curve General Medicine Subarachnoid Hemorrhage medicine.disease Prognosis Surgery Neurology (clinical) Artificial intelligence business Tomography X-Ray Computed computer 030217 neurology & neurosurgery |
| Zdroj: | Ramos, L A, van der Steen, W E, Sales Barros, R, Majoie, C B L M, van den Berg, R, Verbaan, D, Vandertop, W P, Zijlstra, I J A J, Zwinderman, A H, Strijkers, G J, Olabarriaga, S D & Marquering, H A 2019, ' Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage ', Journal of neurointerventional surgery, vol. 11, no. 5, pp. 497-502 . https://doi.org/10.1136/neurintsurg-2018-014258 Journal of neurointerventional surgery, 11(5), 497-502. BMJ Publishing Group |
| ISSN: | 1759-8478 |
| Popis: | Background and purposeDelayed cerebral ischemia (DCI) is a severe complication in patients with aneurysmal subarachnoid hemorrhage. Several associated predictors have been previously identified. However, their predictive value is generally low. We hypothesize that Machine Learning (ML) algorithms for the prediction of DCI using a combination of clinical and image data lead to higher predictive accuracy than previously applied logistic regressions.Materials and methodsClinical and baseline CT image data from 317 patients with aneurysmal subarachnoid hemorrhage were included. Three types of analysis were performed to predict DCI. First, the prognostic value of known predictors was assessed with logistic regression models. Second, ML models were created using all clinical variables. Third, image features were extracted from the CT images using an auto-encoder and combined with clinical data to create ML models. Accuracy was evaluated based on the area under the curve (AUC), sensitivity and specificity with 95% CI.ResultsThe best AUC of the logistic regression models for known predictors was 0.63 (95% CI 0.62 to 0.63). For the ML algorithms with clinical data there was a small but statistically significant improvement in the AUC to 0.68 (95% CI 0.65 to 0.69). Notably, aneurysm width and height were included in many of the ML models. The AUC was highest for ML models that also included image features: 0.74 (95% CI 0.72 to 0.75).ConclusionML algorithms significantly improve the prediction of DCI in patients with aneurysmal subarachnoid hemorrhage, particularly when image features are also included. Our experiments suggest that aneurysm characteristics are also associated with the development of DCI. |
| Databáze: | OpenAIRE |
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